Functional PCA
fit_fdapca <- function(
df, time_column, value_column, replicate_column,
feat_column = NULL, feat = NULL,
cluster = FALSE, cmethod = "EMCluster", K = 2, filter = TRUE,
thresh = 0, num_nonzero = 10, clust_min_num_replicate = 10,
fpca_optns = NULL, fclust_optns = NULL)
{
if(!is.null(feat) & !is.null(feat_column)) {
df <- df %>%
filter_at(.vars = feat_column, any_vars((.) == feat))
}
y_lst <- plyr::dlply(df, replicate_column, function(x) x[[value_column]])
t_lst <- plyr::dlply(df, replicate_column, function(x) x[[time_column]])
if (filter){
idx <- sapply(y_lst, function(y){sum(abs(y) > thresh) >= num_nonzero})
y_lst <- y_lst[idx]
t_lst <- t_lst[idx]
}
if(length(y_lst) == 0) return(NULL)
feat_res <- NULL
if(cluster & (length(y_lst) >= clust_min_num_replicate)) {
feat_res <- try(fdapace::FClust(
y_lst, t_lst, k = K, optnsFPCA = fpca_optns, optnsCS = fclust_optns))
} else {
if(is.null(fpca_optns)) {
fpca_optns <- list()
}
if (length(y_lst) < clust_min_num_replicate) {
fpca_optns$dataType <- 'Sparse'
}
feat_res <- fdapace::FPCA(y_lst, t_lst, optns = fpca_optns)
if(cluster){
feat_res <- list("cluster" = rep(1, length(y_lst)),
"fpca" = feat_res, "clusterObj" = NULL)
}
}
feat_res
}
fitted_values_fpca <- function(obj, derOptns = list(p = 0))
{
selectedK <- NULL; clust_df <- NULL
if("cluster" %in% names(obj)){
clust_df <- data.frame(
"Replicate_ID" = names(obj$fpca$inputData$Ly),
"Cluster" = as.character(obj[["cluster"]]),
stringsAsFactors = FALSE)
obj <- obj[["fpca"]]
}
if(derOptns$p > 0) {
selectedK <- fdapace::SelectK(obj)$K
if(!is.finite(selectedK)) selectedK <- ncol(obj$xiEst)
}
fit <- fdapace:::fitted.FPCA(obj, K = selectedK, derOptns = derOptns)
rownames(fit) <- names(obj$inputData$Ly)
colnames(fit) <- obj$workGrid
fit <- reshape2::melt(fit, varnames = c("Replicate_ID", "time")) %>%
mutate(Replicate_ID = as.character(Replicate_ID))
if(!is.null(clust_df)){
fit <- suppressMessages(fit %>% left_join(clust_df))
}
return(fit)
}
fit_dist_to_baseline <- function(
dist_to_baseline, time_column="RelDay",
value_column = "dist_to_baseline", replicate_column = "Subject")
{
nGrid <- length(
seq(min(dist_to_baseline[[time_column]]),
max(dist_to_baseline[[time_column]])))
fpca_res <- fit_fdapca(
dist_to_baseline, time_column, value_column, replicate_column,
cluster = FALSE, filter = FALSE, fpca_optns = list(nRegGrid = nGrid))
fitted_mean <- data.frame(time = fpca_res$workGrid, value = fpca_res$mu)
fitted_response <- fitted_values_fpca(fpca_res) %>%
mutate(Subject = Replicate_ID) %>%
left_join(
fitted_values_fpca(fpca_res, derOptns = list(p = 1)) %>%
mutate(Subject = Replicate_ID) %>%
rename("deriv" = value)
)
return(list(res = fpca_res, mean = fitted_mean, fitted = fitted_response))
}
# this is because some subjects have multiple samples take the same day
bray_to_baseline_fltr <- bray_to_baseline %>%
group_by(perturbation, Subject, Group, Interval, RelDay) %>%
summarise(n = n(), dist_to_baseline = mean(dist_to_baseline)) %>%
ungroup()
bray_to_baseline_fltr %>% filter(n > 1)
Antibiotics
bray_to_baseline_fltr %>%
filter(perturbation == "Abx") %>%
filter(RelDay >= -50, RelDay <= 60) %>%
mutate(Interval = factor(Interval, level = names(abx_intv_cols))) %>%
ggplot(
aes(x = RelDay, y = dist_to_baseline,
group = Subject, color = Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.5, size = 1.2) +
scale_color_manual(values = abx_intv_cols) +
theme(legend.position = "bottom") +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from initial antibiotic dose") +
ylab("Bray-Curtis distance to 7 pre-antibiotic samples")

fpca.bray.abx <- fit_dist_to_baseline(
bray_to_baseline_fltr %>% filter(perturbation == "Abx", RelDay >= -50, RelDay <= 60))
save(list = c("fpca.bray.abx"), file = "output/fpca_res.rda")
(pAbx <- bray_to_baseline_fltr %>%
filter(perturbation == "Abx", RelDay >= -50, RelDay <= 60) %>%
ggplot(aes(x = RelDay, y = dist_to_baseline)) +
geom_line(
data = fpca.bray.abx[["fitted"]],
aes(group = Subject, x = time, y = value),
alpha = 0.3, size = 0.7, color = "grey30") +
geom_vline(xintercept = 0, lwd = 1, color = "orange") +
geom_vline(xintercept = 4, lwd = 1, color = "orange") +
geom_point(aes(color = Interval), size = 1.5, alpha = 0.7) +
geom_line(
data = fpca.bray.abx[["mean"]], aes(x = time, y = value),
color = "navy", size = 2) +
scale_color_manual(values = abx_intv_cols, name = "Interval") +
scale_x_continuous(
name = "Days from initial antibiotic dose",
limits = c(NA, NA), breaks = seq(-50, 60, 10)) +
scale_y_continuous(
name = "Bray-Curtis distance to baseline",
limits = c(0.1, 0.85), breaks = seq(0.1, 0.80, 0.1)) +
theme(text = element_text(size = 20)))

fpca.bray.abx[["fitted"]] <- fpca.bray.abx[["fitted"]] %>%
mutate(
Interval = ifelse(fpca.bray.abx[["fitted"]]$time < 0 , "PreAbx",
ifelse(fpca.bray.abx[["fitted"]]$time >= 0 & fpca.bray.abx[["fitted"]]$time <= 4, "MidAbx",
"PostAbx")))
(pAbxDeriv <- fpca.bray.abx[["fitted"]] %>%
ggplot(aes(x = RelDay)) +
geom_line(
aes(group = Subject, x = time, y = deriv, color = Interval),
alpha = 0.5, size = 0.7) +
geom_vline(xintercept = 0, lwd = 1, color = "orange") +
geom_vline(xintercept = 4, lwd = 1, color = "orange") +
scale_color_manual(values = abx_intv_cols, name = "Interval") +
scale_x_continuous(name = "",
limits = c(NA, NA), breaks = seq(-50, 60, 10)) +
ylab("Derivative"))

(pAbxLab <- bray_to_baseline_fltr %>%
filter(perturbation == "Abx", RelDay >= -50, RelDay <= 60) %>%
ggplot(aes(x = RelDay, y = dist_to_baseline)) +
geom_line(
data = fpca.bray.abx[["fitted"]],
aes(group = Subject, x = time, y = value),
alpha = 0.3, size = 0.7 ) +
geom_vline(xintercept = 0, lwd = 1, color = "orange") +
geom_vline(xintercept = 4, lwd = 1, color = "orange") +
geom_text(
aes(label = Subject, color = Interval), size = 4, alpha = 0.7) +
geom_line(
data = fpca.bray.abx[["mean"]], aes(x = time, y = value),
color = "navy", size = 2) +
scale_color_manual(values = abx_intv_cols, name = "Interval") +
scale_x_continuous(
name = "Days from initial antibiotic dose",
limits = c(NA, NA), breaks = seq(-50, 60, 10)) +
scale_y_continuous(
name = "Bray-Curtis distance to baseline",
limits = c(0.1, 0.85), breaks = seq(0.1, 0.80, 0.1)) +
theme(text = element_text(size = 20)))

# geom_point(
# data = abx_bray %>% filter(RelDay > StabTime),
# color = "orange", size = 2.5) +
vp = grid::viewport(width = 0.3, height = 0.32, x = 0.07, y =0.95, just = c("left", "top"))
print(pAbx)
print(pAbxDeriv + theme_bw(base_size = 10) + theme_subplot, vp = vp)

Diet
bray_to_baseline_fltr %>%
filter(perturbation == "Diet", RelDay >= -30, RelDay <= 30) %>%
mutate(Interval = factor(Interval, level = names(diet_intv_cols))) %>%
ggplot(
aes(x = RelDay, y = dist_to_baseline,
group = Subject, color = Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.5, size = 1.2) +
scale_color_manual(values = diet_intv_cols) +
theme(legend.position = "bottom") +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from diet initiation") +
ylab("Bray-Curtis distance to 7 pre-diet samples")

fpca.bray.diet30 <- fit_dist_to_baseline(
bray_to_baseline_fltr %>% filter(perturbation == "Diet", RelDay >= -30, RelDay <= 30))
save(list = c("fpca.bray.abx", "fpca.bray.diet", "fpca.bray.diet30"), file = "output/fpca_res.rda")
(pDiet <- bray_to_baseline_fltr %>%
filter(perturbation == "Diet", RelDay >= -30, RelDay <= 30) %>%
ggplot(aes(x = RelDay, y = dist_to_baseline)) +
geom_line(
data = fpca.bray.diet30[["fitted"]],
aes(group = Subject, x = time, y = value),
alpha = 0.3, size = 0.7, color = "grey30") +
geom_vline(xintercept = 0, lwd = 1, color = "orange") +
geom_vline(xintercept = 4, lwd = 1, color = "orange") +
geom_point(aes(color = Interval), size = 1.5, alpha = 0.7) +
geom_line(
data = fpca.bray.diet30[["mean"]], aes(x = time, y = value),
color = "navy", size = 2) +
scale_color_manual(values = diet_intv_cols, name = "Interval") +
scale_x_continuous(
name = "Days from initial antibiotic dose",
limits = c(NA, NA), breaks = seq(-50, 60, 10)) +
scale_y_continuous(
name = "Bray-Curtis distance to baseline",
limits = c(NA, NA), breaks = seq(0.1, 0.80, 0.1)) +
theme(text = element_text(size = 20)))

fpca.bray.diet30[["fitted"]] <- fpca.bray.diet30[["fitted"]] %>%
mutate(
Interval = ifelse(fpca.bray.diet30[["fitted"]]$time < 0 , "PreDiet",
ifelse(fpca.bray.diet30[["fitted"]]$time >= 0 & fpca.bray.diet30[["fitted"]]$time <= 4, "MidDiet",
"PostDiet")))
(pDietDeriv <- fpca.bray.diet30[["fitted"]] %>%
ggplot(aes(x = RelDay)) +
geom_line(
aes(group = Subject, x = time, y = deriv, color = Interval),
alpha = 0.5, size = 0.7) +
geom_vline(xintercept = 0, lwd = 1, color = "orange") +
geom_vline(xintercept = 4, lwd = 1, color = "orange") +
scale_color_manual(values = diet_intv_cols, name = "Interval") +
scale_x_continuous(name = "",
limits = c(NA, NA), breaks = seq(-50, 60, 10)) +
ylab("Derivative"))

(pDietLab <- bray_to_baseline_fltr %>%
filter(perturbation == "Diet", RelDay >= -50, RelDay <= 60) %>%
ggplot(aes(x = RelDay, y = dist_to_baseline)) +
geom_line(
data = fpca.bray.diet[["fitted"]],
aes(group = Subject, x = time, y = value),
alpha = 0.3, size = 0.7 ) +
geom_vline(xintercept = 0, lwd = 1, color = "orange") +
geom_vline(xintercept = 4, lwd = 1, color = "orange") +
geom_text(
aes(label = Subject, color = Interval), size = 4, alpha = 0.7) +
geom_line(
data = fpca.bray.diet[["mean"]], aes(x = time, y = value),
color = "navy", size = 2) +
scale_color_manual(values = diet_intv_cols, name = "Interval") +
scale_x_continuous(
name = "Days from initial antibiotic dose",
limits = c(NA, NA), breaks = seq(-50, 60, 10)) +
scale_y_continuous(
name = "Bray-Curtis distance to baseline",
limits = c(0.1, 0.85), breaks = seq(0.1, 0.80, 0.1)) +
theme(text = element_text(size = 20)))

vp = grid::viewport(width = 0.3, height = 0.32, x = 0.07, y =0.95, just = c("left", "top"))
print(pDiet)
print(pDietDeriv + theme_bw(base_size = 10) + theme_subplot, vp = vp)

Colon cleanout
bray_to_baseline_fltr %>%
filter(perturbation == "CC", RelDay >= -50, RelDay <= 50) %>%
mutate(Interval = factor(Interval, level = names(cc_intv_cols))) %>%
ggplot(
aes(x = RelDay, y = dist_to_baseline,
group = Subject, color = Interval)) +
geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) +
geom_point(alpha = 0.5, size = 1.2) +
scale_color_manual(values = cc_intv_cols) +
theme(legend.position = "bottom") +
guides(colour = guide_legend(override.aes = list(size=3))) +
xlab("Days from diet initiation") +
ylab("Bray-Curtis distance to 7 pre-diet samples")

fpca.bray.cc30 <- fit_dist_to_baseline(
bray_to_baseline_fltr %>%
filter(perturbation == "CC", RelDay >= -30, RelDay <= 30) %>%
arrange(Subject, RelDay))
Joining, by = c("Replicate_ID", "time", "Subject")
save(list = c("fpca.bray.abx", "fpca.bray.diet","fpca.bray.diet30",
"fpca.bray.cc", "fpca.bray.cc30"),
file = "output/fpca_res.rda")
(pCC <- bray_to_baseline_fltr %>%
filter(perturbation == "CC", RelDay >= -30, RelDay <= 30) %>%
ggplot(aes(x = RelDay, y = dist_to_baseline)) +
geom_line(
data = fpca.bray.cc30[["fitted"]],
aes(group = Subject, x = time, y = value),
alpha = 0.3, size = 0.7, color = "grey30") +
geom_vline(xintercept = 0, lwd = 1, color = "orange") +
geom_vline(xintercept = 4, lwd = 1, color = "orange") +
geom_point(aes(color = Interval), size = 1.5, alpha = 0.7) +
geom_line(
data = fpca.bray.cc30[["mean"]], aes(x = time, y = value),
color = "navy", size = 2) +
scale_color_manual(values = cc_intv_cols, name = "Interval") +
scale_x_continuous(
name = "Days from initial antibiotic dose",
limits = c(NA, NA), breaks = seq(-50, 60, 10)) +
scale_y_continuous(
name = "Bray-Curtis distance to baseline",
limits = c(NA, NA), breaks = seq(0.1, 0.80, 0.1)) +
theme(text = element_text(size = 20)))

fpca.bray.cc30[["fitted"]] <- fpca.bray.cc30[["fitted"]] %>%
mutate(Interval = ifelse(fpca.bray.cc30[["fitted"]]$time < 0 , "PreCC","PostCC"))
(pCCDeriv <- fpca.bray.cc30[["fitted"]] %>%
ggplot(aes(x = RelDay)) +
geom_line(
aes(group = Subject, x = time, y = deriv, color = Interval),
alpha = 0.5, size = 0.7) +
geom_vline(xintercept = 0, lwd = 1, color = "orange") +
geom_vline(xintercept = 4, lwd = 1, color = "orange") +
scale_color_manual(values = cc_intv_cols, name = "Interval") +
scale_x_continuous(name = "",
limits = c(NA, NA), breaks = seq(-50, 60, 10)) +
ylab("Derivative"))

(pCCLab <- bray_to_baseline_fltr %>%
filter(perturbation == "CC", RelDay >= -50, RelDay <= 50) %>%
ggplot(aes(x = RelDay, y = dist_to_baseline)) +
geom_line(
data = fpca.bray.cc[["fitted"]],
aes(group = Subject, x = time, y = value),
alpha = 0.3, size = 0.7 ) +
geom_vline(xintercept = 0, lwd = 1, color = "orange") +
geom_vline(xintercept = 4, lwd = 1, color = "orange") +
geom_text(
aes(label = Subject, color = Interval), size = 4, alpha = 0.7) +
geom_line(
data = fpca.bray.cc[["mean"]], aes(x = time, y = value),
color = "navy", size = 2) +
scale_color_manual(values = cc_intv_cols, name = "Interval") +
scale_x_continuous(
name = "Days from initial antibiotic dose",
limits = c(NA, NA), breaks = seq(-50, 60, 10)) +
scale_y_continuous(
name = "Bray-Curtis distance to baseline",
limits = c(0.1, 0.85), breaks = seq(0.1, 0.80, 0.1)) +
theme(text = element_text(size = 20)))

vp = grid::viewport(width = 0.3, height = 0.32, x = 0.07, y =0.95, just = c("left", "top"))
print(pCC)
print(pCCDeriv + theme_bw(base_size = 10) + theme_subplot, vp = vp)

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /share/software/user/open/openblas/0.2.19/lib/libopenblasp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] fdapace_0.4.1 forcats_0.4.0 stringr_1.4.0 dplyr_0.8.3 purrr_0.3.2 readr_1.3.1
[7] tidyr_0.8.3 tibble_2.1.3 ggplot2_3.2.0 tidyverse_1.2.1.9000 RColorBrewer_1.1-2 phyloseq_1.26.1
loaded via a namespace (and not attached):
[1] nlme_3.1-140 fs_1.3.1 lubridate_1.7.4 httr_1.4.0 numDeriv_2016.8-1.1 tools_3.5.1
[7] backports_1.1.4 utf8_1.1.4 R6_2.4.0 vegan_2.5-5 rpart_4.1-15 Hmisc_4.2-0
[13] DBI_1.0.0 lazyeval_0.2.2 BiocGenerics_0.28.0 mgcv_1.8-28 colorspace_1.4-1 permute_0.9-5
[19] ade4_1.7-13 nnet_7.3-12 withr_2.1.2 tidyselect_0.2.5 gridExtra_2.3 compiler_3.5.1
[25] cli_1.1.0 rvest_0.3.4 Biobase_2.42.0 htmlTable_1.13.1 xml2_1.2.0 labeling_0.3
[31] scales_1.0.0 checkmate_1.9.4 digest_0.6.20 foreign_0.8-71 rmarkdown_1.14 XVector_0.22.0
[37] htmltools_0.3.6 base64enc_0.1-3 pkgconfig_2.0.2 dbplyr_1.4.2 htmlwidgets_1.3 rlang_0.4.0
[43] readxl_1.3.1 rstudioapi_0.10 generics_0.0.2 jsonlite_1.6 acepack_1.4.1 magrittr_1.5
[49] Formula_1.2-3 biomformat_1.10.1 Matrix_1.2-17 fansi_0.4.0 Rcpp_1.0.1 munsell_0.5.0
[55] S4Vectors_0.20.1 Rhdf5lib_1.4.3 ape_5.3 stringi_1.4.3 yaml_2.2.0 MASS_7.3-51.4
[61] zlibbioc_1.28.0 rhdf5_2.26.2 plyr_1.8.4 grid_3.5.1 parallel_3.5.1 crayon_1.3.4
[67] lattice_0.20-38 Biostrings_2.50.2 haven_2.1.1 splines_3.5.1 multtest_2.38.0 hms_0.5.0
[73] zeallot_0.1.0 knitr_1.23 pillar_1.4.2 igraph_1.2.4.1 reshape2_1.4.3 codetools_0.2-16
[79] stats4_3.5.1 reprex_0.3.0 glue_1.3.1 evaluate_0.14 latticeExtra_0.6-28 data.table_1.12.2
[85] modelr_0.1.4 vctrs_0.2.0 foreach_1.4.4 cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1
[91] xfun_0.8 broom_0.5.2 pracma_2.2.5 survival_2.44-1.1 iterators_1.0.10 IRanges_2.16.0
[97] cluster_2.1.0
---
title: "Functional PCA"
output: html_notebook
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library("phyloseq")
library("RColorBrewer")
library("tidyverse")
library("fdapace")

datadir <- "../../data/"
curdir <- getwd()
theme_set(theme_bw())
theme_update(text = element_text(20))

theme_subplot <- theme(
      legend.position = "none",
      panel.grid.major = element_blank(), 
      panel.grid.minor = element_blank(),
      panel.background = element_rect(fill = "transparent",colour = NA),
      plot.background = element_rect(fill = "transparent",colour = NA)
    )

abx_intv_cols <- c("PreAbx" = "grey60", "MidAbx" = "#E41A1C", 
                   "PostAbx" = "#00BFC4", "UnpAbx" = "Purple")
diet_intv_cols <- c("PreDiet" = "grey60", "MidDiet" = "#FD8D3C", 
                    "PostDiet" = "#4DAF4A")
cc_intv_cols <- c("PreCC" = "grey60", "PostCC" = "#7A0177") #"#AE017E")

intv_cols <- c(abx_intv_cols, diet_intv_cols, cc_intv_cols, "NoInterv" = "grey60")
```



# Load Data

```{r}
# File generated in /perturbation_16s/analysis/analysis_summer2019/generate_phyloseq.rmd
psSubj <- readRDS("../../data/16S/phyloseq/perturb_physeq_participants_decontam_15Jul19.rds")
psSubj
# otu_table()   OTU Table:         [ 2425 taxa and 4402 samples ]
# sample_data() Sample Data:       [ 4402 samples by 40 sample variables ]
SMP <- data.frame(sample_data(psSubj))
SUBJ <- SMP %>% select(Subject, Age:BirthYear) %>% distinct()
```


```{r}
load("output/pairwise_dist_to_baseline_subj_16S.rda")
load("output/fpca_res.rda")
ls()
```


# Functional PCA 


```{r}
fit_fdapca <- function(
  df, time_column, value_column, replicate_column,
  feat_column = NULL, feat = NULL,
  cluster = FALSE, cmethod = "EMCluster", K = 2, filter = TRUE,
  thresh = 0, num_nonzero = 10, clust_min_num_replicate = 10,
  fpca_optns = NULL, fclust_optns = NULL)
{
  if(!is.null(feat) & !is.null(feat_column)) {
    df <- df %>% 
      filter_at(.vars = feat_column, any_vars((.) == feat))
  }
  y_lst <- plyr::dlply(df, replicate_column, function(x) x[[value_column]])
  t_lst <- plyr::dlply(df, replicate_column, function(x) x[[time_column]])
  if (filter){
    idx <- sapply(y_lst, function(y){sum(abs(y) > thresh) >= num_nonzero})
    y_lst <- y_lst[idx]
    t_lst <- t_lst[idx]
  }  
  if(length(y_lst) == 0) return(NULL)
  feat_res <- NULL
  if(cluster & (length(y_lst) >= clust_min_num_replicate)) {
    feat_res <- try(fdapace::FClust(
      y_lst, t_lst, k = K, optnsFPCA = fpca_optns, optnsCS = fclust_optns))
  } else {
    if(is.null(fpca_optns)) {
      fpca_optns <- list()
    } 
    if (length(y_lst) < clust_min_num_replicate) {
      fpca_optns$dataType <- 'Sparse'
    }
    feat_res <- fdapace::FPCA(y_lst, t_lst, optns = fpca_optns)
    if(cluster){
      feat_res <- list("cluster" = rep(1, length(y_lst)), 
                       "fpca" = feat_res, "clusterObj" = NULL)
    }
  }
  feat_res
}

fitted_values_fpca <- function(obj, derOptns = list(p = 0)) 
{
  selectedK <- NULL; clust_df <- NULL
  if("cluster" %in% names(obj)){
    clust_df <- data.frame(
      "Replicate_ID" = names(obj$fpca$inputData$Ly),
      "Cluster" =  as.character(obj[["cluster"]]),
      stringsAsFactors = FALSE)
    obj <- obj[["fpca"]]
  }
  if(derOptns$p > 0) {
    selectedK <- fdapace::SelectK(obj)$K
    if(!is.finite(selectedK)) selectedK <- ncol(obj$xiEst)
  }  
  fit <- fdapace:::fitted.FPCA(obj, K = selectedK, derOptns = derOptns)
  rownames(fit) <- names(obj$inputData$Ly)
  colnames(fit) <- obj$workGrid
  fit <- reshape2::melt(fit, varnames = c("Replicate_ID", "time")) %>%
    mutate(Replicate_ID = as.character(Replicate_ID))
  if(!is.null(clust_df)){
    fit <- suppressMessages(fit  %>% left_join(clust_df))
  }
  return(fit)
}


fit_dist_to_baseline <- function(
  dist_to_baseline, time_column="RelDay", 
  value_column = "dist_to_baseline", replicate_column = "Subject") 
{
  nGrid <- length(
    seq(min(dist_to_baseline[[time_column]]),
        max(dist_to_baseline[[time_column]])))
  fpca_res <- fit_fdapca(
    dist_to_baseline, time_column, value_column, replicate_column,
    cluster = FALSE, filter = FALSE, fpca_optns = list(nRegGrid = nGrid))
  
  fitted_mean <- data.frame(time = fpca_res$workGrid, value = fpca_res$mu)
  fitted_response <- fitted_values_fpca(fpca_res) %>%
    mutate(Subject = Replicate_ID) %>%
    left_join(
      fitted_values_fpca(fpca_res, derOptns = list(p = 1)) %>%
        mutate(Subject = Replicate_ID) %>%
        rename("deriv" = value)
    )
  return(list(res = fpca_res, mean = fitted_mean, fitted = fitted_response))
}
```


```{r}
# this is because some subjects have multiple samples take the same day
bray_to_baseline_fltr <- bray_to_baseline %>% 
  group_by(perturbation, Subject, Group, Interval, RelDay) %>%
  summarise(n = n(), dist_to_baseline = mean(dist_to_baseline)) %>%
  ungroup()

bray_to_baseline_fltr %>% filter(n > 1)

```

## Antibiotics


```{r bray-abx}
bray_to_baseline_fltr %>% 
  filter(perturbation == "Abx") %>%
  filter(RelDay >= -50, RelDay <= 60) %>%
  mutate(Interval = factor(Interval, level = names(abx_intv_cols))) %>%
  ggplot(
    aes(x = RelDay, y = dist_to_baseline, 
        group = Subject, color = Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.5, size = 1.2) + 
  scale_color_manual(values = abx_intv_cols) + 
  theme(legend.position = "bottom") + 
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from initial antibiotic dose") +
  ylab("Bray-Curtis distance to 7 pre-antibiotic samples") 
```


```{r, eval = FALSE}
fpca.bray.abx <- fit_dist_to_baseline(
  bray_to_baseline_fltr %>% filter(perturbation == "Abx", RelDay >= -50, RelDay <= 60))

save(list = c("fpca.bray.abx"), file = "output/fpca_res.rda")
```

```{r fpca-bray-abx, fig.width=12, fig.height=6.5}
(pAbx <- bray_to_baseline_fltr %>% 
  filter(perturbation == "Abx", RelDay >= -50, RelDay <= 60) %>%
ggplot(aes(x = RelDay, y = dist_to_baseline)) +
  geom_line(
    data = fpca.bray.abx[["fitted"]],
    aes(group = Subject, x = time, y = value),
    alpha = 0.3, size = 0.7, color = "grey30") +
  geom_vline(xintercept = 0, lwd = 1, color = "orange") +
  geom_vline(xintercept = 4, lwd = 1, color = "orange") +
  geom_point(aes(color = Interval), size = 1.5, alpha = 0.7) +
  geom_line(
    data = fpca.bray.abx[["mean"]], aes(x = time, y = value),
    color = "navy", size = 2) +
  scale_color_manual(values = abx_intv_cols, name = "Interval") +
  scale_x_continuous(
    name = "Days from initial antibiotic dose",
    limits = c(NA, NA), breaks = seq(-50, 60, 10)) +
  scale_y_continuous(
    name = "Bray-Curtis distance to baseline", 
    limits = c(0.1, 0.85), breaks = seq(0.1, 0.80, 0.1)) +
  theme(text = element_text(size = 20)))
```


```{r fpca-deriv-bray-abx, fig.width=12, fig.height=6.5}
fpca.bray.abx[["fitted"]] <- fpca.bray.abx[["fitted"]] %>%
  mutate(
    Interval = ifelse(fpca.bray.abx[["fitted"]]$time < 0 , "PreAbx",
               ifelse(fpca.bray.abx[["fitted"]]$time >= 0 & fpca.bray.abx[["fitted"]]$time <= 4, "MidAbx",
                      "PostAbx")))

(pAbxDeriv <-  fpca.bray.abx[["fitted"]] %>%
  ggplot(aes(x = RelDay)) +
  geom_line(
    aes(group = Subject, x = time, y = deriv, color = Interval),
    alpha = 0.5, size = 0.7) +
  geom_vline(xintercept = 0, lwd = 1, color = "orange") +
  geom_vline(xintercept = 4, lwd = 1, color = "orange") +
  scale_color_manual(values = abx_intv_cols, name = "Interval") +
  scale_x_continuous(name = "",
    limits = c(NA, NA), breaks = seq(-50, 60, 10)) +
  ylab("Derivative"))
```


```{r fpca-bray-abx-labs, fig.width=12, fig.height=6.5}
(pAbxLab <- bray_to_baseline_fltr %>% 
  filter(perturbation == "Abx", RelDay >= -50, RelDay <= 60) %>%
ggplot(aes(x = RelDay, y = dist_to_baseline)) +
  geom_line(
    data = fpca.bray.abx[["fitted"]],
    aes(group = Subject, x = time, y = value),
    alpha = 0.3, size = 0.7 ) +
  geom_vline(xintercept = 0, lwd = 1, color = "orange") +
  geom_vline(xintercept = 4, lwd = 1, color = "orange") +
  geom_text(
      aes(label = Subject, color = Interval), size = 4, alpha = 0.7) +
  geom_line(
    data = fpca.bray.abx[["mean"]], aes(x = time, y = value),
    color = "navy", size = 2) +
  scale_color_manual(values = abx_intv_cols, name = "Interval") +
  scale_x_continuous(
    name = "Days from initial antibiotic dose",
    limits = c(NA, NA), breaks = seq(-50, 60, 10)) +
  scale_y_continuous(
    name = "Bray-Curtis distance to baseline", 
    limits = c(0.1, 0.85), breaks = seq(0.1, 0.80, 0.1)) +
  theme(text = element_text(size = 20)))

  # geom_point(
  #     data = abx_bray %>% filter(RelDay > StabTime),
  #     color = "orange", size = 2.5) +
```

```{r fpca-bray-abx-val-deriv, fig.width=12, fig.height=6.5}
vp = grid::viewport(width = 0.3, height = 0.32, x = 0.07, y =0.95, just = c("left", "top"))
print(pAbx)
print(pAbxDeriv + theme_bw(base_size = 10) + theme_subplot, vp = vp)
```



## Diet



```{r}
bray_to_baseline_fltr %>% 
  filter(perturbation == "Diet", RelDay >= -30, RelDay <= 30) %>%
  mutate(Interval = factor(Interval, level = names(diet_intv_cols))) %>%
  ggplot(
    aes(x = RelDay, y = dist_to_baseline, 
        group = Subject, color = Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.5, size = 1.2) + 
  scale_color_manual(values = diet_intv_cols) + 
  theme(legend.position = "bottom") + 
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from diet initiation") +
  ylab("Bray-Curtis distance to 7 pre-diet samples") 
```


```{r, eval = FALSE}
fpca.bray.diet30 <- fit_dist_to_baseline(
  bray_to_baseline_fltr %>% filter(perturbation == "Diet", RelDay >= -30, RelDay <= 30))

save(list = c("fpca.bray.abx", "fpca.bray.diet", "fpca.bray.diet30"), file = "output/fpca_res.rda")
```

```{r fpca-bray-diet, fig.width=12, fig.height=6.5}
(pDiet <- bray_to_baseline_fltr %>% 
  filter(perturbation == "Diet", RelDay >= -30, RelDay <= 30) %>%
ggplot(aes(x = RelDay, y = dist_to_baseline)) +
  geom_line(
    data = fpca.bray.diet30[["fitted"]],
    aes(group = Subject, x = time, y = value),
    alpha = 0.3, size = 0.7, color = "grey30") +
  geom_vline(xintercept = 0, lwd = 1, color = "orange") +
  geom_vline(xintercept = 4, lwd = 1, color = "orange") +
  geom_point(aes(color = Interval), size = 1.5, alpha = 0.7) +
  geom_line(
    data = fpca.bray.diet30[["mean"]], aes(x = time, y = value),
    color = "navy", size = 2) +
  scale_color_manual(values = diet_intv_cols, name = "Interval") +
  scale_x_continuous(
    name = "Days from initial antibiotic dose",
    limits = c(NA, NA), breaks = seq(-50, 60, 10)) +
  scale_y_continuous(
    name = "Bray-Curtis distance to baseline", 
    limits = c(NA, NA), breaks = seq(0.1, 0.80, 0.1)) +
  theme(text = element_text(size = 20))) 
```


```{r fpca-deriv-bray-diet, fig.width=12, fig.height=6.5}
fpca.bray.diet30[["fitted"]] <- fpca.bray.diet30[["fitted"]] %>%
  mutate(
    Interval = ifelse(fpca.bray.diet30[["fitted"]]$time < 0 , "PreDiet",
               ifelse(fpca.bray.diet30[["fitted"]]$time >= 0 & fpca.bray.diet30[["fitted"]]$time <= 4, "MidDiet",
                      "PostDiet")))

(pDietDeriv <-  fpca.bray.diet30[["fitted"]] %>%
  ggplot(aes(x = RelDay)) +
  geom_line(
    aes(group = Subject, x = time, y = deriv, color = Interval),
    alpha = 0.5, size = 0.7) +
  geom_vline(xintercept = 0, lwd = 1, color = "orange") +
  geom_vline(xintercept = 4, lwd = 1, color = "orange") +
  scale_color_manual(values = diet_intv_cols, name = "Interval") +
  scale_x_continuous(name = "",
    limits = c(NA, NA), breaks = seq(-50, 60, 10)) +
  ylab("Derivative"))
```


```{r fpca-bray-diet-labs, fig.width=12, fig.height=6.5}
(pDietLab <- bray_to_baseline_fltr %>% 
  filter(perturbation == "Diet", RelDay >= -50, RelDay <= 60) %>%
ggplot(aes(x = RelDay, y = dist_to_baseline)) +
  geom_line(
    data = fpca.bray.diet[["fitted"]],
    aes(group = Subject, x = time, y = value),
    alpha = 0.3, size = 0.7 ) +
  geom_vline(xintercept = 0, lwd = 1, color = "orange") +
  geom_vline(xintercept = 4, lwd = 1, color = "orange") +
  geom_text(
      aes(label = Subject, color = Interval), size = 4, alpha = 0.7) +
  geom_line(
    data = fpca.bray.diet[["mean"]], aes(x = time, y = value),
    color = "navy", size = 2) +
  scale_color_manual(values = diet_intv_cols, name = "Interval") +
  scale_x_continuous(
    name = "Days from initial antibiotic dose",
    limits = c(NA, NA), breaks = seq(-50, 60, 10)) +
  scale_y_continuous(
    name = "Bray-Curtis distance to baseline", 
    limits = c(0.1, 0.85), breaks = seq(0.1, 0.80, 0.1)) +
  theme(text = element_text(size = 20)))

```


```{r fpca-bray-diet-val-deriv, fig.width=12, fig.height=6.5}
vp = grid::viewport(width = 0.3, height = 0.32, x = 0.07, y =0.95, just = c("left", "top"))
print(pDiet)
print(pDietDeriv + theme_bw(base_size = 10) + theme_subplot, vp = vp)
```

## Colon cleanout



```{r bray-cc}
bray_to_baseline_fltr %>% 
  filter(perturbation == "CC", RelDay >= -50, RelDay <= 50) %>%
  mutate(Interval = factor(Interval, level = names(cc_intv_cols))) %>%
  ggplot(
    aes(x = RelDay, y = dist_to_baseline, 
        group = Subject, color = Interval)) +
  geom_line(aes(group = Subject), alpha = 0.7, lwd = 0.5) + 
  geom_point(alpha = 0.5, size = 1.2) + 
  scale_color_manual(values = cc_intv_cols) + 
  theme(legend.position = "bottom") + 
  guides(colour = guide_legend(override.aes = list(size=3))) +
  xlab("Days from diet initiation") +
  ylab("Bray-Curtis distance to 7 pre-diet samples") 
```


```{r}
fpca.bray.cc30 <- fit_dist_to_baseline(
  bray_to_baseline_fltr %>% 
    filter(perturbation == "CC", RelDay >= -30, RelDay <= 30) %>%
    arrange(Subject, RelDay))

save(list = c("fpca.bray.abx", "fpca.bray.diet","fpca.bray.diet30", 
              "fpca.bray.cc", "fpca.bray.cc30"), 
     file = "output/fpca_res.rda")
```

```{r fpca-bray-cc, fig.width=12, fig.height=6.5}
(pCC <- bray_to_baseline_fltr %>% 
  filter(perturbation == "CC", RelDay >= -30, RelDay <= 30) %>%
ggplot(aes(x = RelDay, y = dist_to_baseline)) +
  geom_line(
    data = fpca.bray.cc30[["fitted"]],
    aes(group = Subject, x = time, y = value),
    alpha = 0.3, size = 0.7, color = "grey30") +
  geom_vline(xintercept = 0, lwd = 1, color = "orange") +
  geom_vline(xintercept = 4, lwd = 1, color = "orange") +
  geom_point(aes(color = Interval), size = 1.5, alpha = 0.7) +
  geom_line(
    data = fpca.bray.cc30[["mean"]], aes(x = time, y = value),
    color = "navy", size = 2) +
  scale_color_manual(values = cc_intv_cols, name = "Interval") +
  scale_x_continuous(
    name = "Days from initial antibiotic dose",
    limits = c(NA, NA), breaks = seq(-50, 60, 10)) +
  scale_y_continuous(
    name = "Bray-Curtis distance to baseline", 
    limits = c(NA, NA), breaks = seq(0.1, 0.80, 0.1)) +
  theme(text = element_text(size = 20))) 
```


```{r fpca-deriv-bray-cc, fig.width=12, fig.height=6.5}
fpca.bray.cc30[["fitted"]] <- fpca.bray.cc30[["fitted"]] %>%
  mutate(Interval = ifelse(fpca.bray.cc30[["fitted"]]$time < 0 , "PreCC","PostCC"))

(pCCDeriv <-  fpca.bray.cc30[["fitted"]] %>%
  ggplot(aes(x = RelDay)) +
  geom_line(
    aes(group = Subject, x = time, y = deriv, color = Interval),
    alpha = 0.5, size = 0.7) +
  geom_vline(xintercept = 0, lwd = 1, color = "orange") +
  geom_vline(xintercept = 4, lwd = 1, color = "orange") +
  scale_color_manual(values = cc_intv_cols, name = "Interval") +
  scale_x_continuous(name = "",
    limits = c(NA, NA), breaks = seq(-50, 60, 10)) +
  ylab("Derivative"))
```


```{r fpca-bray-cc-labs, fig.width=12, fig.height=6.5}
(pCCLab <- bray_to_baseline_fltr %>% 
  filter(perturbation == "CC", RelDay >= -50, RelDay <= 50) %>%
ggplot(aes(x = RelDay, y = dist_to_baseline)) +
  geom_line(
    data = fpca.bray.cc[["fitted"]],
    aes(group = Subject, x = time, y = value),
    alpha = 0.3, size = 0.7 ) +
  geom_vline(xintercept = 0, lwd = 1, color = "orange") +
  geom_vline(xintercept = 4, lwd = 1, color = "orange") +
  geom_text(
      aes(label = Subject, color = Interval), size = 4, alpha = 0.7) +
  geom_line(
    data = fpca.bray.cc[["mean"]], aes(x = time, y = value),
    color = "navy", size = 2) +
  scale_color_manual(values = cc_intv_cols, name = "Interval") +
  scale_x_continuous(
    name = "Days from initial antibiotic dose",
    limits = c(NA, NA), breaks = seq(-50, 60, 10)) +
  scale_y_continuous(
    name = "Bray-Curtis distance to baseline", 
    limits = c(0.1, 0.85), breaks = seq(0.1, 0.80, 0.1)) +
  theme(text = element_text(size = 20)))

```


```{r fpca-bray-cc-val-deriv, fig.width=12, fig.height=6.5}
vp = grid::viewport(width = 0.3, height = 0.32, x = 0.07, y =0.95, just = c("left", "top"))
print(pCC)
print(pCCDeriv + theme_bw(base_size = 10) + theme_subplot, vp = vp)
```

```{r}
sessionInfo()
```

